Current 3D scene segmentation methods are heavily dependent on manually
annotated 3D training datasets. Such manual annotations are labor-intensive,
and often lack fine-grained details. Importantly, models trained on this data
typically struggle to recognize object classes beyond the annotated classes,
i.e., they do not generalize well to unseen domains and require additional
domain-specific annotations. In contrast, 2D foundation models demonstrate
strong generalization and impressive zero-shot abilities, inspiring us to
incorporate these characteristics from 2D models into 3D models. Therefore, we
explore the use of image segmentation foundation models to automatically
generate training labels for 3D segmentation. We propose Segment3D, a method
for class-agnostic 3D scene segmentation that produces high-quality 3D
segmentation masks. It improves over existing 3D segmentation models
(especially on fine-grained masks), and enables easily adding new training data
to further boost the segmentation performance -- all without the need for
manual training labels.

通过引入 2D 模型的特性，我们提出了 Segment3D 方法，用于生成高质量的 3D 场景分割掩码，从而实现对于细粒度掩码的改进，并且能够轻松添加新的训练数据以进一步提升分割性能，而无需手动标注训练标签。

Segment3D: 无需手动标注的细粒度类别无关三维分割学习

Segment3D: Learning Fine-Grained Class-Agnostic 3D Segmentation without  Manual Labels

We present a method for generating consistent novel views from a single
source image. Our approach focuses on maximizing the reuse of visible pixels
from the source image. To achieve this, we use a monocular depth estimator that
transfers visible pixels from the source view to the target view. Starting from
a pre-trained 2D inpainting diffusion model, we train our method on the
large-scale Objaverse dataset to learn 3D object priors. While training we use
a novel masking mechanism based on epipolar lines to further improve the
quality of our approach. This allows our framework to perform zero-shot novel
view synthesis on a variety of objects. We evaluate the zero-shot abilities of
our framework on three challenging datasets: Google Scanned Objects, Ray Traced
Multiview, and Common Objects in 3D. See our webpage for more details:
this https URL

从单一源图像生成一致的新视图的方法，利用单目深度估计器将源视图的可见像素转移到目标视图，并通过基于外极线的新的屏蔽机制进一步改善方法的质量，从而在各种对象上实现零样本新视图合成。

iNVS：借用扩散修复算法实现新视角生成

iNVS: Repurposing Diffusion Inpainters for Novel View Synthesis

Modern large language models (LLMs) have demonstrated impressive capabilities
at sophisticated tasks, often through step-by-step reasoning similar to humans.
This is made possible by their strong few and zero-shot abilities -- they can
effectively learn from a handful of handcrafted, completed responses
("in-context examples"), or are prompted to reason spontaneously through
specially designed triggers. Nonetheless, some limitations have been observed.
First, performance in the few-shot setting is sensitive to the choice of
examples, whose design requires significant human effort. Moreover, given the
diverse downstream tasks of LLMs, it may be difficult or laborious to handcraft
per-task labels. Second, while the zero-shot setting does not require
handcrafting, its performance is limited due to the lack of guidance to the
LLMs. To address these limitations, we propose Consistency-based Self-adaptive
Prompting (COSP), a novel prompt design method for LLMs. Requiring neither
handcrafted responses nor ground-truth labels, COSP selects and builds the set
of examples from the LLM zero-shot outputs via carefully designed criteria that
combine consistency, diversity and repetition. In the zero-shot setting for
three different LLMs, we show that using only LLM predictions, COSP improves
performance up to 15% compared to zero-shot baselines and matches or exceeds
few-shot baselines for a range of reasoning tasks.

本研究提出了一种基于一致性的自适应提示设计方法，可以从大型语言模型的零样本输出中选择和构建示例，进而显著提高了零样本情况下三种不同大型语言模型的推理任务的性能。

自适应提示提升零样本推理能力

Better Zero-Shot Reasoning with Self-Adaptive Prompting

Videos of actions are complex signals containing rich compositional structure
in space and time. Current video generation methods lack the ability to
condition the generation on multiple coordinated and potentially simultaneous
timed actions. To address this challenge, we propose to represent the actions
in a graph structure called Action Graph and present the new ``Action Graph To
Video'' synthesis task. Our generative model for this task (AG2Vid)
disentangles motion and appearance features, and by incorporating a scheduling
mechanism for actions facilitates a timely and coordinated video generation. We
train and evaluate AG2Vid on the CATER and Something-Something V2 datasets, and
show that the resulting videos have better visual quality and semantic
consistency compared to baselines. Finally, our model demonstrates zero-shot
abilities by synthesizing novel compositions of the learned actions. For code
and pretrained models, see the project page this https URL

本文首次提出 Action Graph 结构来实现多协调及可能同时进行的时间动作相关视频的生成方式，通过 AG2Vid 模型实现了运动和外观特征的分离，并引入了调度机制以协助视频生成，经过对 CATER 和 Something-Something V2 数据集的训练和评估，证明与基线相比，所生成的视频具有更好的视觉质量和语义一致性。同时，该模型还展现了零样本能力，在生成已学习动作的新组合等方面表现出良好的效果。